Prediction of quarter level subclinical mastitis by combining in-line and on-animal sensor data
Momena Khatun A B C , Peter C. Thomson A , Cameron E. F. Clark A and Sergio C. García A
+ Author Affiliations
- Author Affiliations
A Dairy Science Group, Faculty of Science, School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Camden, NSW 2570, Australia.
B Bangladesh Agricultural University, Mymensingh 2202, Bangladesh.
C Corresponding author. Email: mkha3293@uni.sydney.edu.au
Animal Production Science 60(1) 180-186 https://doi.org/10.1071/AN18578
Submitted: 9 September 2018 Accepted: 1 July 2019 Published: 2 December 2019
Abstract
We investigated the potential for automatic detection of subclinical mastitis (SCM) in pasture-based automatic milking systems. The objective of the study was to determine the ability of electrical conductivity (EC), together with relative changes in daily activity (activity) and daily rumination (rumination) recorded using heat and rumination–long-distance tags, to predict quarter-level SCM. Activity (arbitrary unit/day) and rumination (min/day) data were determined across 21 days using heat and rumination–long-distance tags for 170 cows. Cows were allocated into the following three groups: SCM (n = 32, EC ≥ 7.5 millisiemens/cm (mS/cm) in one or more quarters and a positive bacteriological culture in the corresponding quarter(s)); true-negative (TN, n = 9, EC ≥ 7.5 mS/cm and a negative culture in all four quarters); and apparently healthy (n = 129, no culture test and EC < 7.5 mS/cm). Group mean differences in activity and rumination were compared using Welch’s t-tests. Logistic mixed models were used to predict SCM by EC, activity and rumination changes before mastitis detection, including parity information between SCM and TN groups. Cow- and quarter-specific information were included as random effects, followed by model assessment by producing receiver operating-characteristic curve and area under the curve (AUC) value. In total, 287 quarters were used in the prediction model, including 143 quarters with a positive culture (Gram-positive; n = 131, Gram-negative; n = 6, mixed; n = 6) and 144 quarters with a negative culture. On average, SCM group had 4.65% greater (P < 0.01) activity and 9.89% greater (P < 0.001) rumination than did the TN group and 11.70% greater (P < 0.001) activity than did the apparently healthy group. A combined model with terms for EC, activity changes, rumination changes prior to detect SCM and parity had a better SCM prediction (AUC = 0.92) ability than did any of them separately (all AUC < 0.8). Hence, we conclude that EC in combination with activity and rumination information can improve the accuracy of prediction of quarter-level SCM.
Additional keywords: automatic milking systems, daily activity, daily rumination, electrical conductivity.
References
Bar D, Gröhn YT, Bennett G, González RN, Hertl JA, Schulte HF, Tauer LW, Welcome FL, Schukken YH (2007) Effect of repeated episodes of generic clinical mastitis on milk yield in dairy cows. Journal of Dairy Science 90, 4643–4653.| Effect of repeated episodes of generic clinical mastitis on milk yield in dairy cows.Crossref | GoogleScholarGoogle Scholar | 17881685PubMed |
Bewley JM, Boyce RE, Hockin J, Munksgaard L, Eicher SD, Einstein ME, Schutz MM (2010) Influence of milk yield, stage of lactation, and body condition on dairy cattle lying behaviour measured using an automated activity monitoring sensor. The Journal of Dairy Research 77, 1–6.
| Influence of milk yield, stage of lactation, and body condition on dairy cattle lying behaviour measured using an automated activity monitoring sensor.Crossref | GoogleScholarGoogle Scholar | 19758477PubMed |
Bradley AJ (2002) Bovine mastitis: an evolving disease. Veterinary Journal 164, 116–128.
| Bovine mastitis: an evolving disease.Crossref | GoogleScholarGoogle Scholar |
Bruckmaier RM, Ontsouka CE, Blum JW (2004) Fractionized milk composition in dairy cows with subclinical mastitis. Veterinarni Medicina 49, 283–290.
| Fractionized milk composition in dairy cows with subclinical mastitis.Crossref | GoogleScholarGoogle Scholar |
Butler D, Cullis B, Gilmour A, Gogel B (2007) ‘ASReml-R reference manual.’ (Department of Primary Industry, Fish: Brisbane)
Chaplin S, Munksgaard L (2001) Evaluation of a simple method for assessment of rising behaviour in tethered dairy cows. Animal Science 72, 191–197.
| Evaluation of a simple method for assessment of rising behaviour in tethered dairy cows.Crossref | GoogleScholarGoogle Scholar |
Clark CEF, Lyons NA, Millapan L, Talukder S, Cronin GM, Kerrisk KL, Garcia SC (2015) Rumination and activity levels as predictors of calving for dairy cows. Animal 9, 691–695.
| Rumination and activity levels as predictors of calving for dairy cows.Crossref | GoogleScholarGoogle Scholar |
Dairy Australia (2018) ‘Australian dairy industry in focus 2018.’Available at https://www.dairyaustralia.com.au/publications/australian-dairy-industry-in-focus-2018?id=B81A5CE26AAE4C0F898E8F3BFF0014D9 [Verified 4 September 2019]
Fogsgaard KK, Røntved CM, Sørensen P, Herskin MS (2012) Sickness behavior in dairy cows during Escherichia coli mastitis. Journal of Dairy Science 95, 630–638.
| Sickness behavior in dairy cows during Escherichia coli mastitis.Crossref | GoogleScholarGoogle Scholar | 22281328PubMed |
García SC, Fulkerson WJ (2005) Opportunities for future Australian dairy systems: a review. Australian Journal of Experimental Agriculture 45, 1041–1055.
| Opportunities for future Australian dairy systems: a review.Crossref | GoogleScholarGoogle Scholar |
Hertl JA, Schukken YH, Bar D, Bennett GJ, González RN, Rauch BJ, Welcome FL, Tauer LW, Gröhn YT (2011) The effect of recurrent episodes of clinical mastitis caused by Gram-positive and Gram-negative bacteria and other organisms on mortality and culling in Holstein dairy cows. Journal of Dairy Science 94, 4863–4877.
| The effect of recurrent episodes of clinical mastitis caused by Gram-positive and Gram-negative bacteria and other organisms on mortality and culling in Holstein dairy cows.Crossref | GoogleScholarGoogle Scholar | 21943738PubMed |
Hogan JS, Gonzalez RN, Harmon RJ, Nickerson SC, Oliver SP, Pankey JW, Smith KL (1999) ‘Laboratory handbook on bovine mastitis.’ (National Mastitis Council: Madison, WI, USA)
Hogeveen H, Kamphuis C, Steeneveld W, Mollenhorst H (2010) Sensors and clinical mastitis: the quest for the perfect alert. Sensors 10, 7991–8009.
| Sensors and clinical mastitis: the quest for the perfect alert.Crossref | GoogleScholarGoogle Scholar | 22163637PubMed |
Kamphuis C, Mollenhorst H, Heesterbeek JAP, Hogeveen H (2010) Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. Journal of Dairy Science 93, 3616–3627.
| Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.Crossref | GoogleScholarGoogle Scholar | 20655431PubMed |
Kester HJ, Sorter DE, Hogan JS (2015) Activity and milk compositional changes following experimentally induced Streptococcus uberis bovine mastitis. Journal of Dairy Science 98, 999–1004.
| Activity and milk compositional changes following experimentally induced Streptococcus uberis bovine mastitis.Crossref | GoogleScholarGoogle Scholar | 25434337PubMed |
Khatun M, Clark CEF, Lyons NA, Thomson PC, Kerrisk KL, García SC (2017) Early detection of clinical mastitis from electrical conductivity data in an automatic milking system. Animal Production Science 57, 1226–1232.
| Early detection of clinical mastitis from electrical conductivity data in an automatic milking system.Crossref | GoogleScholarGoogle Scholar |
Khatun M, Thomson PC, Kerrisk KL, Lyons NA, Clark CEF, Molfino J, García SC (2018) Development of a new clinical mastitis detection method for automatic milking systems. Journal of Dairy Science 101, 9385–9395.
| Development of a new clinical mastitis detection method for automatic milking systems.Crossref | GoogleScholarGoogle Scholar | 30055925PubMed |
Kitchen BJ (1981) Review of the progress of dairy science: bovine mastitis: milk compositional changes and related diagnostic tests. The Journal of Dairy Research 48, 167–188.
| Review of the progress of dairy science: bovine mastitis: milk compositional changes and related diagnostic tests.Crossref | GoogleScholarGoogle Scholar | 7021617PubMed |
Koop G, van Werven T, Roffel S, Hogeveen H, Nazmi K, Bikker FJ (2015) Short communication: Protease activity measurement in milk as a diagnostic test for clinical mastitis in dairy cows. Journal of Dairy Science 98, 4613–4618.
| Short communication: Protease activity measurement in milk as a diagnostic test for clinical mastitis in dairy cows.Crossref | GoogleScholarGoogle Scholar | 25981067PubMed |
Medrano-Galarza C, Gibbons J, Wagner S, de Passillé AM, Rushen J (2012) Behavioral changes in dairy cows with mastitis. Journal of Dairy Science 95, 6994–7002.
| Behavioral changes in dairy cows with mastitis.Crossref | GoogleScholarGoogle Scholar | 23040015PubMed |
Mein GA, Rasmussen MD (2008) Performance evaluation of systems for automated monitoring of udder health: would the real gold standard please stand up? In ‘Mastitis control: from science to practice’. (Ed. TJGM Lam) pp. 259–266. (Wageningen Academic Publishers: Wageningen, The Netherlands)
Milner P, Page KL, Hillerton JE (1997) The effects of early antibiotic treatment following diagnosis of mastitis detected by a change in the electrical conductivity of milk. Journal of Dairy Science 80, 859–863.
| The effects of early antibiotic treatment following diagnosis of mastitis detected by a change in the electrical conductivity of milk.Crossref | GoogleScholarGoogle Scholar | 9178126PubMed |
Molfino J, Clark CEF, Kerrisk KL, Garciá SC (2017) Evaluation of an activity and rumination monitor in dairy cattle grazing two types of forages. Animal Production Science 57, 1557–1562.
Mollenhorst H, Rijkaart LJ, Hogeveen H (2012) Mastitis alert preferences of farmers milking with automatic milking systems. Journal of Dairy Science 95, 2523–2530.
| Mastitis alert preferences of farmers milking with automatic milking systems.Crossref | GoogleScholarGoogle Scholar | 22541479PubMed |
Nielen M, Schukken YH, Van de Broek J, Brand A, Deluyker HA, Maatje K (1993) Relations between on-line electrical conductivity and daily milk production on a low somatic cell count farm. Journal of Dairy Science 76, 2589–2596.
| Relations between on-line electrical conductivity and daily milk production on a low somatic cell count farm.Crossref | GoogleScholarGoogle Scholar | 8227659PubMed |
Norberg E, Hogeveen H, Korsgaard IR, Friggens NC, Sloth KHMN, Løvendahl P (2004) Electrical conductivity of milk: ability to predict mastitis status. Journal of Dairy Science 87, 1099–1107.
| Electrical conductivity of milk: ability to predict mastitis status.Crossref | GoogleScholarGoogle Scholar | 15259246PubMed |
Pyörälä S (2003) Indicators of inflammation in the diagnosis of mastitis. Veterinary Research 34, 565–578.
| Indicators of inflammation in the diagnosis of mastitis.Crossref | GoogleScholarGoogle Scholar | 14556695PubMed |
Sargeant JM, Leslie KE, Shirley JE, Pulkrabek BJ, Lim GH (2001) Sensitivity and specificity of somatic cell count and California mastitis test for identifying intramammary infection in early lactation. Journal of Dairy Science 84, 2018–2024.
| Sensitivity and specificity of somatic cell count and California mastitis test for identifying intramammary infection in early lactation.Crossref | GoogleScholarGoogle Scholar | 11573781PubMed |
Schukken YH, Hertl J, Bar D, Bennett GJ, González RN, Rauch BJ, Santisteban C, Schulte HF, Tauer L, Welcome FL, Gröhn YT (2009) Effects of repeated Gram-positive and Gram-negative clinical mastitis episodes on milk yield loss in Holstein dairy cows. Journal of Dairy Science 92, 3091–3105.
| Effects of repeated Gram-positive and Gram-negative clinical mastitis episodes on milk yield loss in Holstein dairy cows.Crossref | GoogleScholarGoogle Scholar | 19528587PubMed |
Schukken YH, Günther JJ, Fitzpatrick J, Fontaine MC, Goetze L, Holst O, Leigh J, Petzl W, Schuberth HJ, Sipka A, Smith DGE, Quesnell R, Watts J, Yancey R, Zerbe H, Gurjar A, Zadoks RN, Seyfert HM (2011) Host-response patterns of intramammary infections in dairy cows. Veterinary Immunology and Immunopathology 144, 270–289.
| Host-response patterns of intramammary infections in dairy cows.Crossref | GoogleScholarGoogle Scholar | 21955443PubMed |
Sherlock R, Hogeveen H, Mein G, Rasmussen MD (2008) Performance evaluation of systems for automated monitoring of udder health: analytical issues and guidelines. In ‘Mastitis control: from science to practice’. (Ed. TJGM Lam) pp. 275–282. (Wageningen Academic Publishers: Wageningen, The Netherlands)
Sørensen LP, Bjerring M, Løvendahl P (2016) Monitoring individual cow udder health in automated milking systems using online somatic cell counts. Journal of Dairy Science 99, 608–620.
| Monitoring individual cow udder health in automated milking systems using online somatic cell counts.Crossref | GoogleScholarGoogle Scholar | 26547650PubMed |
Stangaferro ML, Wijma R, Caixeta LS, Al-Abri MA, Giordano JO (2016) Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part III. Metritis. Journal of Dairy Science 99, 7422–7433.
| Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part III. Metritis.Crossref | GoogleScholarGoogle Scholar | 27372583PubMed |
